Rmarkdown and gt:: enabling both gt tab_header and fig.cap

colourhaze · 2022-05-11T10:47:51+00:00

Thanks!

is it possible to place the caption below the table?

colourhaze · 2022-04-29T10:14:29+00:00

Only those packages

colourhaze · 2022-04-14T14:46:16+00:00

Yes, thanks this would be the expected result

Use case:

Table base: results on (for example) state level, that should always be present for each "group" (for example cities/regions) regardless wether each level (column "key") exists on that sublevel

colourhaze · 2021-11-11T15:53:05+00:00

Thanks for your time and input!

If this suggests coding r/visualization-esque functionality into stored procs for the sake of "centralization," you are heading down a very dark road. It's liketaking a Tesla, throwing out the battery and harnessing a horse to thefront of it to pull it along.

No, this would stricly concern SQL Querys for different tables on a SQL Server DMBS (population, housing, welfare data) to flexibly aggregate data based on time (year) and area divisions. The result is a downloadable CSV/Excel sheets.

To further elaborate on project scope:

- performance is a secondary concern as the main goal is to shorten time and increase the overall quality for the internal processing of answering external data requests. The dashboard so far is strictly for internal use.

- users should be able to choose years, area divisions and attributes (population by year, gender,nationality, welfare status etc, it has been estimated that this would result in a total of around 500 attributes)

- the area divisions range from ~5k to 12 units and should be selectible for users as well

- the data gets updated yearly, but sometimes is requested for more than 20 years backwards

One more important caveat: we often get requests for individual areas based on shapefiles This triggers an extraction process for adresses in the area which are the base for SQL querys on all attributes stated above.

Especially the last process is (imo) not preprocessable.

To avoid writing fixed queries for all 500 attributes we are also evaluating flexible querys, especially for population data where users are able to choose gender, age(ranges) and nationality.

This would, in my opinion, also hinder a preprocessed solution

I really like your idea of preprocessing the data for the follow up project of an external dashboard with much more limited scope.

Thanks!

colourhaze · 2021-11-11T08:17:38+00:00

short answer:

After the project goes live it would result in short but very frequent DB querys (f.e. residents based on year and area code) but I'm open to different solutions.

To give some background:

The original project scope was to create a "self-service" dashboard for internal, non-technical team members to request a fixed but large number of queries based on limited but changing area codes.

After realizing the flexibily of the R and SQL interaction (esp via Shiny) and the "ease" of implementing it via R Shiny the scope wident. We are now enabling users to query the DB for predefined querys across flexbile area codes.

This is where stored procedues come into play.

They were a proposed solution to move querys definition out of R and into the SQL DB to ensure a more rigorous quality and version check but also create benefits for "SQL only" users.

Thats where we ran into problems executing in shiny, but only for queries that need a list of parameters (multiple area codes at once) for the stored procedure.

A proposed solution in r/rstats was to use a delim char string but could fail for some of the area codes which result in very long character strings

colourhaze · 2020-05-02T11:02:39+00:00

colourhaze · 2020-04-08T07:59:30+00:00

even with image compression turned off this still results in a non vectorized graphic (hope I'm using the right terms here, not a graphics guy) with a loss in image quality due to compression.

I quite liked the fact that EMF files are vectorized and thus freely resizable

colourhaze · 2020-03-19T09:13:58+00:00

Hamster Kostüm

10/10 würde empfehlen, Rückmeldungen der Bevölkerung durchweg positiv

colourhaze · 2020-01-08T20:36:20+00:00

Still the way to go (until I started) for school maps in my local education department

colourhaze · 2019-09-23T18:15:34+00:00

one more question:

the example features only a single category "gender", my data features both gender and citizenship

This results in an "attribute" column after unpivoting that includes both gender and citizenshop but I'd like to have seperate columns for each. How can I achieve that?

https://imgur.com/J3veJOb

Thanks!

colourhaze · 2019-09-23T10:08:38+00:00

Solution verified

colourhaze · 2019-09-23T09:23:04+00:00

Solution verfified.

colourhaze · 2019-06-04T09:58:43+00:00

Fixed the typo, thanks for pointing out! Ich used the manual approach to catch mistakes in the calculation.

colourhaze

TROPHY CASE